Search Results (335)

Distributed energy systems are increasingly important for strengthening critical infrastructure resilience under conditions of technological, climatic, geopolitical, and cyber disruption. However, existing research on energy resilience is still dominated by technical approaches focused on reliability, renewable energy integration, microgrid control, and storage optimisation, while the role of distributed energy systems in the full crisis-management cycle remains insufficiently conceptualised. This article addresses this gap by combining a scoping review, lexicographic and semantic analysis using IRaMuTeQ version 0.7 alpha 2, and a conceptual-methodological framework for assessing distributed energy systems as instruments of crisis management. The main contribution of the study is the M_ZK-DES model, which integrates technological-infrastructural, decision-operational, legal-institutional, and socio-organisational dimensions with four crisis-management phases: prevention, preparedness, response, and recovery. The model distinguishes distributed energy systems, distributed energy resources, distributed generation, microgrids, prosumers, energy communities, and energy clusters and links them to measurable resilience indicators. These include SAIDI, SAIFI, energy not supplied, restoration time, share of critical load served, islanding capability, voltage and frequency stability, storage autonomy, procedural readiness, and local coordination capacity. The analysis shows that distributed energy systems may reduce vulnerability to cascading failures, support islanded operation, protect vulnerable consumers, improve emergency power continuity, and strengthen local energy autonomy. The proposed scoring and weighting logic enables future empirical validation, scenario testing, and comparative assessment across regions and crisis types, including extreme weather events, cyberattacks, and supply-chain disruptions. The article contributes to energy resilience and crisis-management studies by offering an integrated and operational framework for evaluating distributed energy systems as practical tools for critical infrastructure protection and continuity of essential public services. Full article

The transition from centralized fossil fuel-based power systems toward decentralized smart grids with a high penetration of renewable energy sources (RES) introduces substantial challenges in monitoring, control, coordination, and management. These challenges are particularly evident at the active power grid periphery, defined in this work as the decentralized edge layer of modern power systems comprising low-voltage distribution networks, distributed energy resources (DERs), prosumers, energy storage systems, electric vehicles (EVs), and localized intelligent control entities operating near the consumer side of the grid. This review systematically examines the role of multi-agent systems (MASs) in addressing these emerging challenges. A total of 160 articles, drawn predominantly from top-tier Q1 journals and published up to March 2026, were systematically analyzed to evaluate recent methodological advances, identify persistent research gaps, and compare existing problem formulations and mathematical techniques. The review covers MAS-based applications including distributed energy management, voltage and frequency regulation, demand-side management, microgrid coordination, EV charging coordination, resilience enhancement, and cyber-physical supervisory control. The findings indicate that although MASs offer enhanced scalability, flexibility, resilience, and decentralized decision-making capabilities, existing approaches continue to face significant limitations associated with communication latency, cybersecurity vulnerabilities, interoperability constraints, heterogeneous agent dynamics, and limited real-time experimental validation. Furthermore, this review proposes six emerging research hypotheses targeting underexplored domains, presents a methodological decision flowchart for MAS implementation and selection, and discusses future research directions involving the integration of digital twins, blockchain technologies, edge intelligence, and advanced communication architectures with MAS frameworks. Full article

This study addresses the multi-objective optimization of characterizing a flat glass processing plant. To assess the operational conditions required for a flat glass processing small and medium-sized enterprise (SME) to become a prosumer compatible with renewable energy community (REC) participation. This work is motivated by the presence of more than 300 SMEs in Italy, like this, where RECs represent one of the few viable strategies for achieving the European Union’s 2050 decarbonization targets. The research is carried out in two scenarios; Scenario-I includes Stage-i and Stage-ii with the mutual goal of forecasting and optimizing. Forecasting is used in Stage-i to optimize the factory load, and in Stage-ii to shift and curtail energy loads based on the forecast, considering the Italian national energy price and the regional price bands (“fasce orarie”) F1, F2, and F3. Forecasting and the indicators of environmental and social performance are the means to ensure the best energy utilization and management, as they prove that the reduction in CO₂ emissions and benefits on the community level can be both obtainable. Subsequently, the techno-economic analysis and evaluation of prosumer-readiness conditions are carried out through the optimization of industrial energy demand: three optimization objectives are assessed in this study (i) energy cost, (ii) carbon emission, and (iii) load curtailment. Four algorithms are put into effect to solve the tri-objective optimization: multi-objective particle swarm optimization (MOPSO), multi-objective ant nesting algorithm (MOANA), non-dominated sorting genetic algorithm (NSGA-II), and multi-objective grey wolf optimization (MOGWO). The algorithms are validated in Stage-ii to find the desired optimum in the cost of energy, reduce peak formation, and carbon emissions. To achieve this goal, a stochastic approach based on Monte Carlo simulations and VIKOR is used to optimally select the results. The findings show that the NSGA-II, MOPSO, and MOANA are more effective in solving the problem, while the MOGWO algorithm more quickly finds the optimal solution. Based on the defined objectives, a new configuration for the energy community is introduced, together with a community well-being index and an evaluation of the resulting benefits for the factory. In Scenario-II, the PV plants’ installation on the factory is sized, and the excess energy shared with the grid is evaluated. The Scenario-II results show that 497.184 MWh (33.9%) of energy is shared with the grid. Both results suggest how optimized industrial demand profiles improve SME participation in future RECs. Full article

The rapid digitalization of energy systems and the increasing integration of distributed energy resources, renewable energy technologies, and prosumer-oriented infrastructures have accelerated the development of IoT-enabled Smart Grids as a foundation for intelligent and adaptive energy management. Modern Smart Grids increasingly depend on the coordinated interaction of IoT architectures, artificial intelligence, distributed analytics, and decentralized control mechanisms to ensure reliability, scalability, and real-time operational flexibility. Despite extensive research activity, existing studies remain predominantly technology-centric, focusing on isolated architectural layers or individual intelligent methods without providing a unified system-level perspective on their coordinated operation and interoperability. This article presents a system-level integrative review and challenge-oriented comparative synthesis of intelligent operational and architectural foundations of IoT-enabled Smart Grids. The study analyzes data-driven, model-driven, knowledge-driven, agent-based, and hybrid-oriented intelligent paradigms within multi-layer IoT energy infrastructures. In addition, the research establishes a cross-layer mapping between Smart Grid operational challenges, enabling technologies, and corresponding analytical approaches while identifying interoperability constraints, scalability limitations, and coordination challenges associated with decentralized energy ecosystems. The conducted synthesis demonstrates that hybrid-oriented intelligent approaches represent the most promising direction for future Smart Grid evolution due to their ability to integrate AI, ML, digital twins, semantic reasoning, and decentralized multi-agent coordination within unified IoT architectures. The conducted comparative synthesis identifies the ongoing transition from isolated intelligent solutions toward integrated hybrid cyber–physical energy ecosystems and highlights key characteristics of future adaptive, interoperable, scalable, and explainable Smart Grid architectures. Full article

With increasing penetration of distributed energy resources, peer-to-peer (P2P) electricity trading has attracted attention for locally utilizing surplus renewable energy. This paper proposes a distributed consensus-based P2P electricity trading method that explicitly considers prosumer equipment operation constraints. Each prosumer autonomously solves a daily scheduling problem considering electricity demand, PV generation, battery operation, grid purchase and sale, and P2P trades with neighboring prosumers. P2P prices and desired trading quantities are iteratively adjusted through local information exchange. After convergence, bidirectional trades are converted into net one-way trades, and the final feasible daily schedule is obtained by re-optimizing with fixed trading quantities. Numerical simulations were conducted for six low-voltage prosumers using annual residential demand data and a representative daily PV generation profile. In the base case, the proposed method reduced annual electricity cost by 13.7% compared with the no-P2P case, while its total cost was only 2.3% higher than that of the centralized benchmark. Unlike the centralized benchmark, which increased costs for some prosumers, the proposed method reduced costs for all prosumers. Wheeling-charge sensitivity analysis showed that the charge affects P2P trading volume and benefit allocation. Future work will address tariff design, PV uncertainty, scalability, and distribution-network constraints. Full article

Energy communities (ECs) are becoming a key institutional instrument for decentralizing the European energy transition, yet their implementation remains constrained by fragmented legal interpretation, uneven national transposition, and unresolved socio-technical coordination problems. This review synthesizes the peer-reviewed literature, EU primary legal texts, and national legislation to clarify the distinction between Renewable Energy Communities (RECs) and Citizen Energy Communities (CECs), alongside the amendment relationship between the RED II and RED III directives. The analysis demonstrates that the scalability of these initiatives depends less on theoretical legal recognition and more on aligning operational frameworks, including metering, settlement, cybersecurity, and equitable allocation rules. The Romanian case illustrates this challenge clearly: rapid prosumer growth creates valuable distributed generation but also exposes physical grid constraints, asymmetric socio-economic participation capacity, and weak experience with cooperative energy governance. To address these vulnerabilities, this paper contributes a focused analytical framework linking energy justice, peer-to-peer game-theoretic modeling, and the strategic integration of “anchor-prosumers.” The study argues that larger renewable self-consumers can act as stabilizing community anchors when internal energy prices are designed between wholesale export values and retail import prices, thereby improving both producer incentives and consumer affordability. Future research developments, including targeted surveys and longitudinal empirical validations, will sustain this claim and optimize the socio-economic resilience of decentralized energy markets. Full article

The increasing adoption of renewable energy sources introduces significant variability in power generation, requiring effective strategies to ensure maintain grid stability. Incentive-based demand response programs provide a practical solution for balancing supply and demand, however disputes may arise over energy data integrity. The existing solutions frequently rely on centralized authorities, exposing a single point of failure, or high costs and privacy limitation of recording granular data on-chain. To address this challenge, we propose a decentralized framework that separates cloud storage from integrity certification. This system employs a community aggregator to collect high-frequency energy measurements, store the raw data in the cloud, while anchors unique cryptographic hashes for batch of raw data to a public blockchain. This process creates an auditable and tamper-evident record of data. By recording only hashes on chain, our approach achieves privacy and scalability. Evaluation using a real-world Australian dataset confirms that the system enables transparent dispute resolution, with blockchain transaction costs consistently representing less than 0.10% of the total incentives awarded to participants. Full article

The integration of renewable energy and rising electricity demand strain system flexibility. While price-based demand response (PBDR) improves flexibility through pricing signals, its efficacy hinges critically on accurate consumer modeling. Recognizing this pivotal role, this paper provides a comprehensive review of consumer models in PBDR and their applications to electric vehicles (EVs). First, a unified conceptual framework is presented, delineating the energy, information and financial flows among the system operator (SO), load aggregators (LAs), and end-users, and highlighting the central position of consumer modeling. Second, existing modeling approaches are systematically classified into four categories, namely rule-based, optimization-based, data-driven, and hybrid, to facilitate the selection of appropriate models by researchers and stakeholders for diverse scenarios. Furthermore, the application and adaptation of these models to EVs are critically analyzed, accounting for unique vehicular constraints. Subsequently, a systematic summary of the key characteristics and existing research gaps is provided. Finally, key directions for future research are proposed accordingly, aimed at incorporating bounded rationality into behavioral models, developing individualized consumer modeling coupled with user-specific dynamic pricing, and extending consumer modeling to residential multi-energy prosumers in integrated energy systems. Full article

This paper introduces the integration of smart appliances and Internet of Things technologies for the local balancing of low-voltage power distribution networks, particularly in response to the proliferation of prosumer renewable energy sources. The primary objective is the incorporation of the Elastic Energy Management algorithm with Mixed Reality and Augmented Reality interfaces to facilitate intuitive demand-side management. The methodology employs the GRASP heuristic algorithm alongside advanced on-device 3D point cloud segmentation, enabling the system to identify physical energy consumers within a residential environment. Simulation results demonstrate high algorithmic convergence and the capacity for the system to provide real-time updates to visual interfaces. The findings indicate that the utilization of AR and MR goggles significantly enhances interaction with energy infrastructure by providing hands-free operation and overlaying digital data directly onto physical components. This approach enables more effective grid balancing and increased self-consumption of renewable energy while maintaining user comfort and reducing the technical knowledge required for efficient household energy management. Full article

Electric power distribution systems have been undergoing a transformation that can be attributed to factors such as the deregulation of the electric power supply industry, growing public concern over energy security and the environmental impact of energy generation and utilization, and technological advancements that have given impetus to concerted efforts to modernize the power grid in the framework of smart grid initiatives. The traditionally passive distribution network is increasingly becoming active due to the steady increase in the amount of distributed energy resources being integrated into the network. This has, in turn, given rise to a higher need for flexibility resources that can be used to handle the increased uncertainty caused by stochastic and intermittent distributed resources, such as variable renewable power generation. The provision of demand-side flexibility has largely been the purview of large industrial and commercial energy consumers. This article discusses the role that the aggregator can play in facilitating the provision of flexibility resources by small-scale consumers and prosumers and presents a case study on small-scale renewable generation and residential demand forecasting, which form an integral part of demand flexibility aggregation. Full article

The photovoltaic (PV) sector is at present one of the crucial components of renewable power engineering and one of the key pillars in the global power system transformation. This article compares the annual energy yields from real-life PV installations built in Częstochowa (Poland)—three stationary PV installations and one tracker PV installation. The PV installations are located within a 2 km radius, and except for very early morning and late evening hours, there is no shading, thus identical solar exposure conditions can be assumed for all analyzed PV installations. In the case of stationary PV installations, maximum energy production may be achieved if the PV modules are southward oriented and related to their tilt angles. In the case of installations on buildings, PV modules are rarely installed in their optimal orientation. Most often, the orientation of PV modules is directly related to the location of the building and the geometric structure of the roof. A tracking system, which involves mounting PV modules on platforms that track the sun’s path, increases energy yield per module power. Limitations for tracking PV systems include the requirement for adequate, shade-free space for their construction as well as high costs of the structure itself and its maintenance. During the period analyzed (2022–2025), no PV system outages resulting from exceeding the permissible voltage in the distribution network were recorded. The energy produced by individual PV systems was also compared with the values calculated in a simulation program used to estimate annual energy yields during the system design phase. Full article

This study presents a comprehensive clustering analysis of residential prosumer profiles for optimizing control and peer-to-peer (P2P) energy trading in community renewable energy systems (CRES). Using data from 25 prosumer households equipped with rooftop solar photovoltaic (PV) systems and electric vehicle (EV) charging capabilities, this study implements and compares k-means and spectral clustering algorithms to identify optimal segmentation strategies for prosumer energy management. K-means clustering identifies seven practical prosumer categories with a silhouette coefficient of 0.17, while spectral clustering achieves superior mathematical separation with a silhouette coefficient of 0.275 in ten clusters, though producing six singleton outliers. The k-means solution demonstrates three primary prosumer categories: net producers, net consumers, and balanced profiles. Cluster size variation requires adaptive optimization, while singleton outliers need custom strategies. EV ownership impact consumption, so future proliferation demands dynamic clustering, and these findings will guide metaheuristic algorithms for energy trading and pricing. Full article

The aggregation of flexible resources contributes to sustainability because it impacts on CO₂ emissions, enables renewable energy integration, improves network efficiency and makes the electric power system more resilient. The research project DEMAND has tested the potential of bottom-up aggregation of smart prosumers with no intermediation by a third-party balancing service provider. The present work analyzes the electrical and environmental effects of this new type of aggregation in different scenarios, taking into account both simulated data and data obtained from four pilot sites where the DEMAND system has been implemented. The effectiveness of the proposed aggregation method is evaluated through the calculation of some KPIs: power peaks, grid losses, voltage drops and CO₂ emissions. Full article

Modern power systems increasingly integrate renewable energy sources (RESs), electric mobility, and dynamic market participation. Dynamic electricity pricing, reflecting real-time market conditions, is increasingly important for prosumers worldwide, enabling flexible and efficient energy management. The growing adoption of electric vehicles (EVs) and bidirectional charging technologies (V2G, V2H) allows EVs to act as mobile battery energy storage systems (mBESSs). This study presents a Python 3.11-based application for simulating and analyzing energy flows in residential systems with photovoltaic (PV) installations, EVs acting as mBESS, and optional stationary battery energy storage systems (BESSs), using real 2024 data on consumption, PV production, and market prices. The energy management system (EMS) employs a rule-based algorithm to optimize energy use and economic benefits, adjusting dispatch between PV systems, the grid, mBESSs, and BESSs based on price coefficients $\alpha$ and $\beta$. Simulation scenarios were developed based on two EV availability patterns: Profile 1, representing users unavailable during standard working hours, and Profile 2, representing users with intermittent availability for brief excursions. The results demonstrate substantial electricity cost reductions: For a Nissan Leaf e+ with Profile 1, annual costs decrease by approximately 20% compared to a system without EVs. With PV generation and Profile 2, costs drop by 57% relative to the baseline, while adding a stationary BESS further reduces costs by nearly 95%. It should be noted that the results were obtained assuming zero energy costs for propulsion. Therefore, the economic benefits reported here represent an upper-bound estimate and would be lower under real-world driving conditions. These findings highlight that coordinated EMS operation with EVs as mBESSs, supported by optional BESSs, can maximize economic performance and provide prosumers with a practical framework for flexible and efficient energy management. Full article

The decarbonization of the residential sector is a critical component of the European Green Deal, particularly in transition economies like Poland. This study proposes a comprehensive techno-economic optimization of a deeply retrofitted single-family house aiming for net-zero energy building (NZEB) status. The research specifically focuses on the Polish coastal climate zone, characterized by distinct humidity, wind, and temperature profiles compared to inland regions, which significantly influence the efficiency of air-to-water heat pumps (ASHP). Based on a real-world energy audit, the study simulates the synergy between a deep thermal envelope upgrade and a hybrid system comprising an ASHP, photovoltaics (PV), and battery energy storage (BES). This paper presents a detailed economic analysis of such hybrid systems under the new Polish ‘net-billing’ prosumer mechanism. The study evaluates the impact of electricity tariff structures (flat-rate G11 vs. time-of-use G12w) on the investment’s profitability. By calculating key performance indicators—including the levelized cost of energy (LCOE), net present value (NPV), and self-sufficiency ratio (SSR)—the research assesses various system configurations. The initial evaluation indicates that while deep retrofitting significantly reduces heating demand, integrating battery storage plays a critical role in enhancing economic returns under the net-billing framework. The analysis demonstrates that the optimized hybrid system (9.0 kWp PV + 10 kWh BESS) achieves an average annual self-sufficiency ratio (SSR) of 49.8% and reduces the non-renewable primary energy (EP) indicator to 0.0 kWh/(m²·year). Economically, the investment yields a positive NPV of €3194, an IRR of 5.25%, and a LCOE of €0.184/kWh, which is 34% lower than projected grid prices. Furthermore, switching to a time-of-use tariff (G12w) generates an additional 11% (€139) in annual savings. These quantitative findings provide actionable guidelines for policymakers and investors, confirming the financial viability and environmental benefit (annual reduction of 6.12 MgCO₂) of NZEB standards in coastal areas. Full article